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Optimizing Agentic Systems

This course guides you through the essentials of optimizing agentic AI systems, including prompt engineering, RAG, and evaluation.
Difficulty: Intermediate
Duration: Up to 4 hours and 23 minutes
Students: 6
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Content overview
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Content overview

7 steps
There are 4 Courses in this learning path
There is 1 Exam in this learning path
There are 2 Hands-on Labs in this learning path
Difficulty: Beginner. Duration: 2m.
COURSE
Beginner
Duration: 2 minutes
Introduction
TBC
Difficulty: Beginner. Duration: 8m 54s.
COURSE
Beginner
Duration: 8 minutes and 54 seconds
Prompting for agentic systems
Learn how to design prompts that shape agent behavior, improve accuracy, and deliver outputs that fit your goals. 
Duration: Up to 10m.
EXAM
Duration: Up to 10 minutes
Knowledge Check: Optimizing Agentic Systems
Knowledge Check: Optimizing Agentic Systems
Difficulty: Beginner. Duration: 11m 53s.
COURSE
Beginner
Duration: 11 minutes and 53 seconds
Retrieval-Augmented Generation
Discover how Retrieval-Augmented Generation combines fresh data with model knowledge to deliver accurate, context-rich answers. 
Difficulty: Intermediate. Duration: Up to 1h 30m.
HANDS-ON LAB
Intermediate
Duration: Up to 1 hour and 30 minutes
Improving Agentic Systems with RAG & Prompt Engineering
Lab for Module 7 of the Building AI Agents course.
Difficulty: Beginner. Duration: 19m 59s.
COURSE
Beginner
Duration: 19 minutes and 59 seconds
Tracing, observability, and monitoring
Learn how tracing and observability give you visibility into agent decisions, helping you monitor, debug, and build safer systems.
Difficulty: Intermediate. Duration: Up to 2h.
HANDS-ON LAB
Intermediate
Duration: Up to 2 hours
Tracing, Observability, Monitoring & Evaluation in Agentic Systems
Learn about monitoring, observability, and evaluation in agentic AI systems using LangSmith and Tavily.